Algorithm 1:

Training: input=training data ${\mathbf{x}}_i$; $i=1,\dots ,N_t$

1:	for $\text{ep}=1,2,\dots$ do
2:	for $i=1,2,\dots ,N_t$ do
3:	Determine ${\mathbf{x}}_i^{\ast }$ using DEQ forward iterations
4:	Determine ${\mathit{\eta }}_i^{\ast }=\arg {\max }_{\mathit{\eta }}p\left({\mathbf{x}}_i^{\ast },\mathit{\eta }\right)$
5:	$C_i=\parallel {\mathbf{x}}_i-{\mathbf{x}}_i^{\ast }{\parallel }^2-\frac{1}{m}\log \left(T-p\left({𝓗}_{\theta }\left({\mathbf{x}}_i^{\ast }\right),{\mathit{\eta }}_i^{\ast }\right)\right)$
6:	Determine ${\nabla }_{\theta }{C}_i$ using DEQ backward iterations
7:	$\theta \leftarrow \theta -\gamma {\nabla }_{\theta }{C}_i$, where $C_i$ is the loss in (27)
8:	end for
9:	end for